Method and device for displaying predicted volume of influence with patient-specific atlas of neural tissue

ABSTRACT

This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. In an example, volumetric imaging data representing an anatomical volume of a brain of a patient can be obtained and transformed to brain atlas data. A patient-specific brain atlas can be created using the inverse of the transformation to map the brain atlas data onto the volumetric imaging data and a volume of influence can be calculated using the patient-specific brain atlas. In certain examples, the volume of influence can include a predicted volume of tissue affected by an electrical stimulation delivered by an electrode at a corresponding at least one candidate electrode target location.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.12/070,521, filed Feb. 19, 2008, now U.S. Pat. No. 7,904,134 which ishereby incorporated herein by reference in its entirety, which is acontinuation of U.S. application Ser. No. 10/885,982 (filed on Jul. 7,2004), now U.S. Pat. No. 7,346,382.

TECHNICAL FIELD

This patent application pertains generally to neurosurgery and moreparticularly, but not by way of limitation, to brain stimulation models,systems, devices, and methods.

BACKGROUND

High frequency deep brain stimulation (DBS), such as of the thalamus orbasal ganglia, represents a clinical technique for the treatment ofdisorders such as essential tremor and Parkinson's disease (PD). Pilotstudies have also begun to examine the utility of DBS for treatingdystonia, epilepsy, and obsessive-compulsive disorder. However,understanding of the therapeutic mechanisms of action remains elusive.It is also unclear what stimulation parameters, electrode geometries, orelectrode locations are better suited for existing or future uses ofDBS.

A DBS procedure typically involves first obtaining preoperative imagesof the patient's brain, such as by using a computed tomography (CT)scanner device, a magnetic resonance imaging (MRI) device, or any otherimaging modality. This sometimes involves first affixing to thepatient's skull spherical or other fiducial markers that are visible onthe images produced by the imaging modality. The fiducial markers helpregister the preoperative images to the actual physical position of thepatient in the operating room during the later surgical procedure.

After the preoperative images are acquired by the imaging modality, theyare then loaded onto an image-guided surgical (IGS) workstation, such asthe StealthStation® from the Surgical Navigation Technologies, Inc.(SNT) subsidiary of Medtronic, Inc., for example. Using the preoperativeimages being displayed on the IGS workstation, the neurosurgeon canselect a target region within the brain, an entry point on the patient'sskull, and a desired trajectory between the entry point and the targetregion. The entry point and trajectory are typically carefully selectedto avoid intersecting or otherwise damaging certain nearby criticalbrain structures.

In the operating room, the patient is immobilized and the patient'sactual physical position is registered to the preoperative imagesdisplayed on the IGS workstation, such as by using a remotely detectableIGS wand. In one example, the physician marks the entry point on thepatient's skull, drills a burr hole at that location, and affixes atrajectory guide device about the burr hole. The trajectory guide deviceincludes a bore that can be aimed using the IGS wand to obtain thedesired trajectory to the target region. After aiming, the trajectoryguide is locked to preserve the aimed trajectory toward the targetregion.

After the aimed trajectory has been locked in using the trajectoryguide, a microdrive introducer is used to insert the surgical instrumentalong the trajectory toward the target region of the brain. The surgicalinstrument may include, among other things, a recording electrodeleadwire, for recording intrinsic electrical brain signals, astimulation electrode leadwire, for providing electrical energy to thetarget region of the brain, or associated auxiliary guide catheters forsteering a primary instrument toward target region of the brain. Therecording electrode leadwire is typically used first to confirm, byinterpreting the intrinsic electrical brain signals, that a particularlocation along the trajectory is indeed the desired target region of thebrain. The stimulation electrode leadwire, which typically includesmultiple closely-spaced electrically independent stimulation electrodecontacts, is then introduced to deliver the therapeutic DBS stimulationto the target region of the brain. The stimulation electrode leadwire isthen immobilized, such as by using an instrument immobilization devicelocated at the burr hole entry in the patient's skull. The actual DBStherapy is often not initiated until a time period of about two-weeks toone month has elapsed. This is due primarily to the acute reaction ofthe brain tissue to the introduced DBS stimulation electrode leadwire(e.g., the formation of adjacent scar tissue), and stabilization of thepatient's disease symptoms. At that time, a particular one of thestimulation electrode contacts is then selected for delivering thetherapeutic DBS stimulation, and other DBS parameters are adjusted toachieve an acceptable level of therapeutic benefit. However, theseparameter selections are typically currently achieved via arbitrarytrial-and-error, without visual aids of the electrode location in thetissue medium or computational models of the volume of tissue influencedby the stimulation.

The subthalamic nucleus (STN) represents the most common target for DBStechnology. Clinically effective STN DBS for PD has typically usedelectrode contacts in the anterior-dorsal STN. However, STN DBS exhibitsa low threshold for certain undesirable side effects, such as tetanicmuscle contraction, speech disturbance and ocular deviation. Highlyanisotropic fiber tracks are located about the STN. Such nerve tracksexhibit high electrical conductivity in a particular direction.Activation of these tracks has been implicated in many of the DBS sideeffects. However, there exists a limited understanding of the neuralresponse to DBS. The three-dimensional (3D) tissue medium near the DBSelectrode typically includes both inhomogeneous and anisotropiccharacteristics. Such complexity makes it difficult to predict theparticular volume of tissue influenced by DBS.

A treating physician typically would like to tailor the DBS parameters(such as which one of the stimulating electrodes to use, the stimulationpulse amplitude, the stimulation pulse width, or the stimulationfrequency) for a particular patient to improve the effectiveness of theDBS therapy. This is a complex problem because there are severaldifferent DBS parameters than can be varied. Because selecting aparticular DBS electrode contact and parameter combination setting istypically a trial-and-error process, it is difficult and time-consumingand, therefore, expensive. Moreover, it may not necessarily result inthe best possible therapy or in avoiding the above-mentioned undesirableside effects. Therefore, there is a need to provide help to speed orotherwise improve this DBS parameter selection process or to otherwiseenhance DBS techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

In the drawings, which are not necessarily drawn to scale, like numeralsdescribe substantially similar components throughout the several views.Like numerals having different letter suffixes represent differentinstances of substantially similar components. The drawings illustrategenerally, by way of example, but not by way of limitation, variousembodiments discussed in the present document.

FIG. 1A is a series of color panels illustrating examples of modeledpotential distributions and second differences of potentialdistributions for various electrode configurations.

FIG. 1B is a series of color panels illustrating examples of a humandiffusion tensor image, a volume ratio map representing a portionthereof, and a modeled potential distribution and second differences ofpotential distributions for various electrode positions.

FIG. 2 is a graph illustrating an example of lateral distance from anelectrode plotted on a x-axis, and axon stimulating threshold voltageplotted on a first y-axis, and the axon stimulating second differencethreshold voltage plotted on a second y-axis.

FIG. 3 is a flow chart illustrating generally one example of a method ofusing a model to calculate a volume of activation, as discussed above.

FIG. 4 is a block diagram illustrating generally one conceptualizationof a system for performing at least some of the methods for deep brainstimulation (DBS) or other stimulation of a patient.

FIG. 5 is a flow chart illustrating generally one example of a method ofusing a model to calculate a volume of activation (VOA), and using theVOA to select a particular electrode morphology.

FIG. 6 is a block diagram illustrating generally one example of acomputer-assisted patient-specific neural stimulation modeling system.

FIG. 7 is a computer display screenshot illustrating one example of amulti-resolution, finite-element tetrahedral mesh.

FIG. 8 is a computer display screenshot illustrating one example of astimulation electrode shaft.

FIG. 9. is a voltage vs. time graph of an exemplary stimulationwaveform.

FIG. 10 is a computer display screenshot illustrating one example of adisplayed the target region together with the model-calculated volume ofinfluence.

DETAILED DESCRIPTION

The following detailed description includes references to theaccompanying drawings, which form a part of the detailed description.The drawings show, by way of illustration, specific embodiments in whichthe invention may be practiced. These embodiments, which are alsoreferred to herein as “examples,” are described in enough detail toenable those skilled in the art to practice the invention. Theembodiments may be combined, other embodiments may be utilized, orstructural, logical and electrical changes may be made without departingfrom the scope of the present invention. The following detaileddescription is, therefore, not to be taken in a limiting sense, and thescope of the present invention is defined by the appended claims andtheir equivalents.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one. In this document, the term“or” is used to refer to a nonexclusive or, unless otherwise indicated.Furthermore, all publications, patents, and patent documents referred toin this document are incorporated by reference herein in their entirety,as though individually incorporated by reference. In the event ofinconsistent usages between this document and those documents soincorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

1. Modeling Techniques

A. Introduction

One fundamental step toward understanding the neural response to DBS ischaracterizing the electric field generated by a stimulating electrode.The electric field is dependent on the shape of the electrode and theelectrical conductivity of the tissue medium. DBS electrodes arethree-dimensional structures and the tissue conductivity of the centralnervous system (CNS) is both inhomogeneous (dependent on location) andanisotropic (dependent on direction). The tissue inhomogeneity andanisotropy surrounding the DBS electrode can alter the shape of theelectric field and the subsequent neural response to stimulation.Therefore, in one example, we employed diffusion tensor imaging (DTI) toestimate the electrical conductivity tensor of the tissue mediumsurrounding one or more DBS electrodes. We incorporated the tissueconductivity data into a finite element model (FEM) tailored toaccurately represent the structure of the particular clinical DBSelectrode and surrounding tissue medium. We then used these models topredict the volume of tissue likely to be affected by typicalstimulation parameters (e.g., stimulation pulse amplitude of betweenabout 1 and 3 Volts, a stimulation pulsewidth of about 0.1 ms, and astimulation frequency of about 150 Hz). We refer to this volume of tissue likely to be affected as the “volume of influence” (VOI) or“volume of activation” (VOA).

B. Exemplary Methods

We developed, among other things, three-dimensional finite elementmodels (FEMs) of the Medtronic 3387-89 DBS lead (Medtronic, Inc.,Minneapolis, Minn.). We examined at least two representations of thenearby tissue electrical properties. In one example, the finite elementmodel used a constant homogenous isotropic tissue conductivity of about0.3 S/m. In another example, the finite element model explicitlyrepresented tissue anisotropy with conductivity tensors (σ) derived fromdiffusion tensor magnetic resonance images. Both examples of finiteelement models used an about 0.2 mm thick sheath of encapsulation tissue(modeled with a conductivity of about 0.15 S/m) about the DBS electrodeleadwire shaft. In one example, the FEM was implemented using 231,404elements in a commercially available software package such as FEMLAB 2.3(COMSOL Inc., Burlington, Mass.). In one example, a 100×100×100 mm³ cubeabout the electrode contact was used as a FEM model boundary, which wasset to a boundary condition of 0 V. In this example of the FEM model,the electrode contact was set to a boundary condition of the DBSstimulus voltage. The potential distribution (V_(e)) generated in thetissue medium was calculated from the Laplace equation:∇·σ∇V _(e)=0,  (Eq. 1)using a Good Broyden iterative solver and algebraic multigridpreconditioner. Doubling the density of the FEM mesh or doubling thedistance of the boundary from the electrode (i.e., quadrupling the sizeof the 100×100×100 mm³ tissue box) yielded a potential distributionV_(e) that differed only by less than 2% when compared to the defaultmodel.

Diffusion tensor imaging (DTI) characterizes the diffusional behavior ofwater in tissue on a voxel-by-voxel basis in terms of a matrix quantityfrom which the diffusion coefficient can be obtained corresponding toany direction in space. The electrical conductivity tensor (σ) of atissue medium is obtainable from the corresponding diffusion tensor (D).The hypothesized relationship between electrical conductivity and waterdiffusion in tissue is prompted by the observation that in a structuredmedium the two processes are related through mutual respect for theboundary conditions imposed by the tissue geometry. In our example, theconductivity tensor σ was directly solved for at each voxel using alinear transform of D:σ=(σ_(e) /d _(e))D,  (Eq. 2)where σ_(e) is the effective extracellular conductivity and d_(e) is theeffective extracellular diffusivity. Our example used a ratio of((σ_(e)/d_(e))=0.736 (S-s)/mm²) as determined from publishedexperimental and empirical data.

In one example, the DTI data was acquired using a 1.5 T Philips GyroscanNT using a single-shot echo-planar imaging (EPI) sequence with the SENSEparallel imaging scheme (SENSitivity Encoding, reduction factor R=2.5).In this example, the imaging matrix was 96×96 with a field of view of240×240 mm, which was zero-filled to 256×256. In this example, axialslices of 2.5 mm thickness were acquired parallel to theanterior-posterior commissure line. In this example, the diffusionweighting was encoded along 30 independent orientations and the b-valuewas 700 s/mm². In this example, the dorsal STN was located in axialslices using stereotactic coordinates and co-registration with theSchlatlenbrand and Bailey [1959] brain atlas. We extracted the DTI datafrom the 10×10 mm region that surrounded our electrode location in theSTN (in this example, the electrode was located 2 mm ventral, 10 mmlateral, and 1 mm posterior to the mid-commissural point). We thentransformed the diffusion tensors to conductivity tensors, as discussedabove. We then incorporated the conductivity tensors into co-registeredsubdomains in the FEM. Then, using the FEM, we solved for the potentialdistribution generated in the tissue medium by DBS, as discussed above.

C. Exemplary Results

Using the exemplary methods discussed above, we compared the electricfield of a theoretical point source, a DBS electrode in an isotropicmedium, and a DBS electrode in an anisotropic medium representative ofthe STN and surrounding tissue structures.

FIG. 1A shows examples of the potential distribution (V_(e)) generatedin the tissue medium for each of these models (first row of FIG. 1A) aswell as the second difference of the potential (Δ²V_(e)) evaluated at0.5 mm increments along different directions (i.e., Δ²V_(e)=V_(e)[n+0.5mm]+V_(e)[n−0.5 mm]−2*V_(e)[n]). The Δ²V_(e) along individual neuronalprocesses induces transmembrane currents. The induced transmembranecurrents result in direct polarization of the neuron by the appliedelectric field. The second difference Δ²V_(e) has both positive andnegative components along any given direction. This results in regionsof both depolarization (positive Δ²V_(e)) and hyperpolarization(negative Δ²V_(e)) in neurons near the electrode, as illustrated in FIG.1A.

FIG. 1A illustrates examples of a potential distribution (V_(e)) and itssecond difference (Δ²V_(e)). FIG. 1A compares (for an isotropic tissuemedium) a monopolar point source (−1 mA stimulation current; illustratedin the left column of FIG. 1A), a monopolar DBS leadwire electrode (−1 Vstimulation voltage, illustrated in the middle two columns of FIG. 1A),and a bipolar DBS leadwire with two electrode contacts (±1 V stimulationvoltages, respectively, illustrated in the right column of FIG. 1A).

The top row of FIG. 1A shows V_(e) over a 10×10 mm² area, with lightercolor tones indicating a more negative potential, and darker color tonesindicating a more positive potential.

The middle row of FIG. 1A shows Δ²V_(e) evaluated along a verticaldirection relative to the displayed plane in the top row of FIG. 1A. Thebottom row of FIG. 1A shows Δ²V_(e) evaluated along a directionperpendicular to the displayed plane in the top row of FIG. 1A. PositiveΔ²V_(e) is representative of a depolarizing influence, which areindicated by redder color tones. Negative Δ²V_(e) is representative of ahyperpolarizing influence, which are indicated by bluer color tones.Δ²V_(e) values>20 mV and <−20 mV are clipped to provide betterresolution of the values of interest.

For the point source, Δ²V_(e) vertical (i.e., the left middle picture ofFIG. 1A) has hyperpolarized top and bottom lobes and depolarized leftand right lobes. For the axial view of the monopolar DBS source, Δ²V_(e)vertical (i.e., the second from left picture in the middle row of FIG.1A) has hyperpolarized top and bottom lobes and depolarized left andright lobes. For the coronal view of the monopolar DBS source, Δ²V_(e)vertical (i.e., the second from right picture in the middle row of FIG.1A) has hyperpolarized top and bottom lobes and depolarized left andright lobes. For the bipolar DBS source, Δ²V_(e) vertical (i.e., theright-most picture in the middle row of FIG. 1A) exhibits, for the upper(positive) electrode, hyperpolarized top and bottom lobes anddepolarized left and right lobes. The bottom lobe is smaller than thetop lobe. In this same picture, the lower (negative) electrode exhibitsdepolarized top and bottom lobes and hyperpolarized left and rightlobes. The top lobe is smaller than the bottom lobe. The Δ²V_(e)perpendicular illustrated by the bottom row of FIG. 1A illustratesdepolarization, except for the lower (negative) electrode of the DBSbipolar source in the right-most picture, which exhibits hyperpolarizedlobes.

FIG. 1B illustrates an example of a monopolar DBS source in ananisotropic medium that is representative of the STN and surroundingtissue structures. The right two panels of the top row of FIG. 1B showthe volume ratio (VR), that is, λ₁ λ₂ λ₃/[(λ₁+λ₂+λ₃)/3]³. The VR is ascalar that quantifies the degree of anisotropy in each voxel using theeigenvalues (λ₁, λ₂, λ₃) of the diffusion tensor, such as for the entireaxial slice of the patient's brain (illustrated in the far right panelof the top row of FIG. 1B) and for the 10×10 region about the STN thatwas used in the FEM (illustrated in the middle right panel of the toprow of FIG. 1B). For this 10×10 region, dark overlaid lines indicate,for reference purposes, the representative anatomical location of theSTN as obtained using the Schlatlenbrand and Bailey brain atlas or thelike.

In the example of FIG. 1B, the middle left panel of the top row showsV_(e) generated from a −1 V stimulus from a monopolar DBS source. Thefar left panel in the top row of FIG. 1B shows Δ²V_(e) evaluated alongthe direction perpendicular to the displayed plane in the middle leftpanel of the top row of FIG. 1B for a −1 V stimulus. The bottom row ofFIG. 1B shows Δ²V_(e) for a −3 V stimulus with the electrode located inthe anterior-dorsal STN (far left panel of the bottom row of FIG. 1B),located 1 mm anterior (middle left panel of the bottom row of FIG. 1B),1 mm medial (middle right panel of the bottom row of FIG. 1B), and 1 mmlateral (far right panel of the bottom row of FIG. 1B).

Because Δ²V_(e) represents the effective volume of activation of nearbytissue, this model can be used to adjust the electrode location orstimulation parameters to obtain a desired volume of activation for theDBS stimulation, such as to activate substantially the entire STN, asillustrated by the location and the −3V stimulation in the bottom farright panel of FIG. 1B, for example.

During extracellular stimulation of the CNS, axonal elements typicallyrepresent the most excitable components of neurons near the electrode.Evaluation of Δ²V_(e) can provide qualitative predictions on thelikelihood of neural activation by an extracellular source. Therefore,to provide a quantitative reference to the Δ²V_(e) data in FIGS. 1A and1B, we used the 5.7 μm diameter myelinated axon model from Cameron C.McIntyre et al., “Modeling the Excitability of Mammalian Nerve Fibers:Influence of Afterpotentials on the Recovery Cycle,” J. Neurophysiology,Vol. 87, February 2002, pp. 995-1006, which is incorporated herein byreference in its entirety, to draw correlations between axonal thresholdand Δ²V_(e). (Alternatively, instead of using an axon model, a moredetailed neuronal model could be used, such as described in Cameron C.McIntyre et al., “Cellular Effects of Deep Brain Stimulation:Model-Based Analysis of Activation and Inhibition,” J. Neurophysiology91: 1457-1469 (2004), which is incorporated by reference herein in itsentirety). A Δx of 0.5 mm was used in evaluating Δ²V_(e) as thisdistance represents the internodal spacing of the 5.7 μm fiber axonmodel. Fifty modeled axons oriented parallel to the electrode shaft wererandomly positioned in the tissue medium surrounding the electrode.Then, the Δ²V_(e) was calculated for threshold stimulation of themodeled axons. The results are illustrated in FIG. 2.

FIG. 2 is a graph that illustrates generally one example of a currentdistance relationship of large diameter axons during DBS. In the exampleof FIG. 2, a threshold stimulus amplitude was calculated for fifty 5.7μm diameter myelinated axons to follow a 150 Hz train of 0.1 ms durationDBS stimulation pulses as a function of their distance from the DBSelectrode. In this example, the axons were randomly positioned in thetissue medium and oriented parallel to the electrode shaft. The seconddifference of the potential distribution (Δx=0.5 mm) at threshold wascalculated for each axon along its path length and at the point in axialplane that slices through the center of the stimulating contact (e.g.,[Δ²V_(e) perpendicular, DBS axial] of FIG. 1A).

This analysis revealed that for a 150 Hz train of cathodic stimuli 0.1ms in duration a Δ²V_(e)>12 mV always generated propagating actionpotentials at the stimulus frequency. When the axon was further than 3mm from the electrode, a Δ²V_(e)>8 mV was enough for activation. Also,in this example, the axon model never blocked firing during −3 V; 0.1ms; 150 Hz stimulation for any of the positions examined.

Returning to FIG. 1A, we evaluated Δ²V_(e) along several directionsrelative to the electrode contact. The respective red and blue ofΔ²V_(e) vertical (shown in middle row in FIG. 1A) represent regions ofrespective depolarization and hyperpolarzation that would be generatedin neural elements running vertically in the displayed plane (i.e., upor down the page). Δ²V_(e) perpendicular (shown in the bottom row ofFIG. 1A) represents regions of depolarization and hyperpolarization thatwould be generated in neural elements running perpendicular to thedisplayed plane (i.e., into or out of the page). Δ²V_(e) typicallyresults in both positive and negative regions in the tissue medium.However, exemplary results from Δ²V_(e) perpendicular (shown in thebottom row of FIG. 1A) only show depolarizing effects. In theseexamples, hyperpolarizing effects still exist in the tissue medium, butbecause of the 3D nature of the stimulation they are not within thefield of view that is shown.

In FIG. 1A, a coupled comparison of (Δ²V_(e) vertical, DBS axial) and(Δ²V_(e) perpendicular, DBS coronal) or (Δ²V_(e) perpendicular, DBSaxial) and (Δ²V_(e) vertical, DBS coronal) show orthogonal planesthrough the center of the electrode contact where Δ²V_(e) has beenevaluated along the same direction relative to the electrode and cangive a sense of the 3D stimulation effects. In this example, the modelresults show that monopolar −1 V stimuli activated large diameter axonswithin about a 2.5 mm radius of the electrode contact, as illustrated byFIG. 1A and FIG. 2. Bipolar stimulation generated a more complex patternof depolarization and hyperpolarization. However, bipolar stimulationdid not dramatically alter the volume of tissue above-threshold foractivating large diameter axons, as shown in FIG. 1A.

FIG. 1B illustrates the incorporation of tissue electrical propertiesrepresentative of the STN and surrounding structures, as discussedabove. As shown in FIG. 1B, this resulted in distortion of V_(e) andΔ²V_(e) generated by DBS as compared to the isotropic case of FIG. 1A.More particularly, the strong dorsal-ventral anisotropy of the internalcapsule (IC) limited stimulation anterior and lateral to the electrode.The moderate anterior-posterior anisotropy of the region around zonaincerta (ZI) extended stimulation posterior to the electrode. Increasingthe stimulus amplitude to −3 V resulted in a volume of activation(represented by the second difference Δ²V_(e)) that was more dependenton the tissue anisotropy and spread outside the borders of the STN. Inaddition, medial-lateral and/or anterior-posterior variation in theelectrode location within STN directly altered the shape and volume ofactivation, as shown in FIG. 1B. An electrode positioned near theanterior and/or lateral borders of the STN exhibited strong activationof IC, while an electrode located in the medial STN resulted in thelargest overall volume of activation and resulted in only limitedactivation of the IC. These results show that a minor change (e.g., onthe order of 1 mm) in the electrode location within the dorsal STN canhave a substantial effect on the neural response to DBS.

D. Discussion of Exemplary Results

DBS represents an effective clinical therapy for movement disorders.However, the existing limited understanding of the effects ofstimulation on the underlying neural tissue hinders future advancementof this technology. The electric field generated by one or more DBSelectrodes, using therapeutic DBS stimulation parameters, can directlyactivate a large volume of tissue, as illustrated by FIGS. 1A, 1B, and2. One example of our model provided results that show that thestimulating effect of the electric field can spread outside the bordersof the dorsal STN and can result in activation of axonal elements in thezona incerta (ZI), fields of Forel (H2), and internal capsule (IC), asshown in FIG. 1B. These model predictions agree with clinical dataindicating that stimulation amplitudes in the range of −3 V are oftencapable of inducing side effects that are associated with activation ofthe corticospinal and corticobulbar tracts of the IC. Our models suggestthat the low threshold side effects of IC stimulation can be avoidedwith electrode locations slightly (e.g., about 1 mm) medial or posteriorto the clinical target of the anterior-dorsal STN. However, given theintrinsic error in the DBS implantation procedure, it is typically notpresently possible to position the electrode with sub-millimeteraccuracy relative to the patient specific neuroanatomy. Also, theclinical effect of the spread of stimulation outside the borders of STNto ZI and H2 is unclear. Although the present model results act as aguide to the spread of stimulation, they may not alter present DBSimplantation procedures. However, the present models may alter futureDBS implantation procedures or present or future DBS parameteradjustments.

The present model provides quantitative results on the effects of DBSthat would be difficult to achieve experimentally. Like most models,however, they involved some simplifying approximations worth noting.First, we used electrostatic analysis and the resolution of ourdiffusion tensor based tissue conductivities was on the order of 1 mm.In general, however CNS tissue typically has a small reactive componentthat results in slight increases in conductivity at higher frequencies.Also, micro-inhomogeneities exist on scales smaller than the 1 mm.However, a reactive component or higher resolution diffusion tensorbased conductivity could be used with the present model techniques, ifdesired.

Second, neural activation that results from applied fields could be moreaccurately predicted by directly coupling the electric field data tomulti-compartment cable models of individual neurons. The present modeltechniques, however, provide easier estimation of the volume of tissuesupra-threshold, and our estimation is derived directly from the fielddata. By evaluating Δ²V_(e) in a plane containing the electrode contact,one can conceptualize the spatial characteristics of the depolarizinginfluence of the field, as illustrated in FIGS. 1A and 1B. By explicitlycalculating the Δ²V_(e) needed to activate large diameter axons (8 mVfor large electrode-to-axon distances), our models provide a worst-casescenario to address the spread of stimulation, as illustrated in FIG. 2,so as to avoid unwanted side effects. This simplified estimation of thespatial extent of DBS on large diameter axons typically has anassociated error of several hundred micrometers. However, given thelarge volume tissue affected by DBS, this error is relatively small,especially for high stimulus amplitudes, as illustrated in FIG. 2.Nonetheless, our model of STN DBS represents a significant improvementover any model that attempts to characterize the spatial extent ofstimulation using empirical observations that ignore the tissueelectrical properties (e.g., inhomogeneity and anisotropy) and electrodegeometry.

Extracellular stimulation typically generates a complex electric fieldin the tissue medium that is applied to the underlying neural processesas a distribution of extracellular potentials. As derived from the cableequation, the second derivative of the extracellular potentials alongeach process will typically produce both transmembrane and axialcurrents that will be distributed throughout the neuron. In turn, eachneuron exposed to the applied field will typically experience bothinward and outward transmembrane currents and regions of depolarizationand hyperpolarization. These theoretical predictions have been verifiedin numerous experimental preparations demonstrating the differencesbetween anodic, cathodic, and bipolar stimulation on the ability to bothactivate and block neural activity with extracellular stimulation.

Analysis of the effects of DBS is complicated by our limitedunderstanding of the response of neurons near the electrode to theapplied fields. Addressing the effects of high frequency DBS presentsinvestigators with a paradox of how stimulation (traditionally thoughtto activate neurons) can result in similar therapeutic outcomes aslesioning target structures in the thalamus or basal ganglia. Thereexist two general philosophies on the effects of DBS: 1) DBS is believedto generate a functional ablation by suppressing or inhibiting thestructure being stimulated or 2) DBS is believed to result in activationpatterns in the stimulated network that override pathological networkactivity. Our model results support the latter theory by showing withdetailed models and therapeutically effective stimulation parametersthat axonal elements are activated over a large volume of tissuesurrounding the electrode.

Experimental investigation on the effects of STN DBS has implicatedactivation of large diameter fiber tracks with therapeutic stimulationparameters. Predictions of the volume of tissue affected by DBS, usingcurrent-density calculations, have suggested that axonal elements wouldbe activated over a 2.5 mm radius of the electrode contact using a −3 Vstimulus. However, current-density is not directly related to the neuralresponse to stimulation, and typically has a non-uniform distribution onDBS electrode contacts. A scaled version of the derivative of thecurrent-density, Δ²V_(e), represents a value that more accuratelyquantifies the stimulating influence of the electric field. UsingΔ²V_(e) in combination with tissue electrical properties derived fromDTI we predict that −3V STN DBS can activate axonal elements in STN, ZI,H2, and IC spreading as far as 4 mm from the electrode contact, asillustrated in FIG. 1B. Furthermore, the anisotropic tissue propertiesnear the STN as well as the electrode location within the STN directlyaffect the size and shape of the activated volume of tissue.

2. Examples of Using a Model

FIG. 3 is a flow chart illustrating generally one example of a method ofusing a model to calculate a volume of activation, as discussed above.Portions of the method may be embodied in any machine-accessible mediumcarrying instructions for executing acts included in the method. Such amethod applies to deep brain stimulation (DBS) or any other electricaltissue stimulation. At 300, imaging data of an anatomic volume of apatient is obtained. In one example, this includes obtaining imagingdata of a patient's brain using an imaging modality, such as computedtomography (CT) or magnetic resonance (MR) imaging modalities, forexample, or another suitable imaging modality. The anatomic volume neednot be all or part of the patient's brain, but could be all or part ofany other anatomic structure.

At 302, in one example, diffusion tensor imaging (DTI) data is obtained(this may occur at 300, such as where a DTI MR imaging modality is usedat 300). In one example, the DTI data is obtained from the same patientbeing analyzed. Alternatively, “atlas” DTI data is obtained from atleast one other patient. If atlas DTI data from another patient is used,it is typically spatially scaled to correspond to the anatomic size andshape of the patient being analyzed. In one example, the atlas DTI datais based on a composite from more than one other patient. The compositeatlas DTI data typically spatially scales DTI data from the differentpatients before combining into the composite DTI atlas. The atlas DTIdata avoids the need to obtain DTI data from the particular patientbeing analyzed. This is useful, for example, when a non-DTI imagingmodality is used at 300.

At 304, a tissue conductivity model is created for all or part of theanatomic volume. The tissue conductivity model is typically anon-uniform spatial distribution. Such a model more accuratelyrepresents inhomogeneous and anisotropic characteristics of the tissueanatomy. For example, the conductivity of brain tissue varies from onebrain region to another. Moreover, conductivity of the nervous system ispreferential to a particular direction that is also dependent on theparticular location in the brain. In one example, a non-uniform tissueconductivity model is created by transforming the DTI data intoconductivity data, such as by using the linear transform techniquesdiscussed above with respect to Equation 2.

It should be noted that it is not required to obtain non-uniform tissueconductivity data using DTI. There exist several alternatives to usingDTI based approximations for the anisotropic and inhomogeneous tissueproperties for the patient specific finite element volume conductormodel. One example technique would be a simple designation of a whitematter and a grey matter conductivity tensor. These two universalconductivity tensors could then be applied to the nodes of the FEM meshusing co-registration with the anatomical MRI. In this manner, theindividual voxels of the MRI data are designated as either white matteror grey matter using post-processing image analysis. Then, each suchvoxel is assigned a conductivity dependent on whether it was classifiedas white matter or grey matter, which white matter voxels having adifferent conductivity value than grey matter voxels. A second exampletechnique would define individual conductivity tensors for designatedbrain regions (e.g., nuclei, sub-nuclei, fiber tracts, etc.). Thismethod would allow for a more detailed representation of the tissueelectrical properties than the first example technique. The conductivitytensor of each designated brain region is defined, in one example, usingexplicit experimental tissue impedance results and anatomicalinformation provided by a human brain atlas. In this technique, theanatomical MRI is sub-divided into different designated brain regions ona voxel-by-voxel basis using post-processing image analysis. Theappropriate conductivity tensors for each designated brain region isthen co-registered with the nodes of the FEM mesh.

At 306, a finite element model (FEM) is created using the conductivitydata obtained at 304. In one example, the FEM model uses a defaultboundary condition that is appropriate for a typical electrode contactmorphology. However, in another example, the FEM model includes anelectrode-specific boundary condition that is tailored to the morphologyof a particular electrode contact or contacts to be used in the DBS orother procedure. The FEM model provides for non-uniform conductivity inthe tissue, such as by using a DTI-derived other conductivity value ateach node in the FEM mesh. The FEM model may include aspects that arenot obtained from the DTI-derived data. In one such example, the FEMmesh models a thin encapsulation sheath about the electrode lead body,as discussed above, which is not derived from the DTI data.

At 308, in one example, the FEM is solved for the electric potentialdistribution or the second difference (Δ²V) of the electric potentialdistribution, as discussed above, such as by using FEM solver software.In one example, the FEM is solved for a normalized stimulation amplitudeof 1V. In another example, for a different electric stimulationamplitude, the resulting electric potential distribution (or seconddifference of the electric potential distribution) is multiplied by ascale ratio of the different electric stimulation amplitude to thenormalized electric stimulation amplitude.

At 310, a volume of activation (VOA) or other volume of influence iscalculated, in one example, using the second difference of the electricpotential distribution. The VOA represents the region in which anyneurons therein are expected to typically be activated, that is, theyare expected to generate propagating action potentials at the stimulusfrequency in response to the electrical stimulation delivered at thestimulation electrode contact. Conversely, neurons outside the VOA areexpected to typically remain unactivated in response to the electricalstimulation. In one example, a particular threshold value of the seconddifference of the electric potential distribution defines the boundarysurface of the VOA.

As discussed above, the particular threshold value defining the boundaryof the VOA is determined as follows. First, model neuronal elements arepositioned relative to the electrode using known neuroanatomicalinformation about specific fiber pathways and nuclei of interest nearthe electrode. These generalized positions of the model neuronalelements are then refined, such as by using explicit “patient-specific”information provided in the DTI or anatomical MR imaging data. Forexample, the DTI imaging data describes the inhomogeneous andanisotropic tissue properties near the electrode. In this example, suchDTI imaging data is used to explicitly define one or more axonaltrajectories, if needed, or to help define nuclear boundaries specifiedin the anatomical MR.

A model of these neurons is then created. In one example, the neuronsare modeled using an axon model, which is a simplified form of a neuronmodel. An example of an axon model is described in Cameron C. McIntyreet al., “Modeling the Excitability of Mammalian Nerve Fibers: Influenceof Afterpotentials on the Recovery Cycle,” J. Neurophysiology, Vol. 87,February 2002, pp. 995-1006, which is incorporated by reference hereinin its entirety, including its disclosure of axon models. In anotherexample, a more generalized neuronal model is used, an example of whichis described in Cameron C. McIntyre et al., “Cellular Effects of DeepBrain Stimulation: Model-Based Analysis of Activation and Inhibition,”J. Neurophysiology, Vol. 91, April 2004, pp. 1457-1469, which isincorporated by reference herein in its entirety, including itsdisclosure of neuronal models. The neuron model describes how theneurons will respond to an applied electric field, that is, whether theneuron will fire and whether the neurons will generate a propagatingaction potential.

In one example, using this neuron model to simulate how the neurons(located as determined from the DTI-derived conductivity data, in oneexample) behave, the threshold value of the second difference ofelectric field that will result in such propagating action potentials iscalculated. The stimulating influence of the electric field is appliedto the model neurons to define a threshold value. This threshold valueis then used to define the boundary of the VOA in the non-uniformconductivity tissue, as discussed above.

It should be noted that the neuron model may depend on one or more ofthe electrical parameters of the DBS stimulation being modeled. Forexample, the stimulation pulsewidth will affect the neuron response.Therefore, in one example, the neuron model is tailored to a specificvalue for one or more DBS stimulation parameters.

It should also be noted that calculation of explicit threshold criteriafor each patient is not required. For example, in a more generalizedsituation, threshold criteria will have already been determined usingthe detailed neuron models under a wide variety of different stimulationconditions. Once these threshold criteria have been determined, theyneed not be re-determined for each subsequent patient.

It should also be noted that using a threshold criteria upon the seconddifference of the potential distribution in the tissue medium is asimplified technique for quickly determining a VOA or other volume ofinfluence. The intermediate step of using the second difference of thepotential distribution is not required. In an alternate example, the FEMmodel of is directly coupled to a detailed neuron model, such as amulti-compartment neuron model that is oriented and positioned in theFEM model to represent at least one actual nerve pathway in the anatomicvolume.

At 312, the calculated VOA region is displayed, such as on a computermonitor. In one example, the VOA is displayed superimposed on thedisplayed imaging data or a volumetric representation derived from suchimaging data. In another example, an anatomic boundary or otherrepresentation of an anatomic structure is superimposed on the VOA andimaging data or the like. The anatomic boundary data is typicallyobtained from an atlas of brain anatomy data, which can be scaled forthe particular patient, as discussed above. Alternatively, the anatomicrepresentation is extracted from the imaging data for the patient beinganalyzed. In one example, the anatomic representation is a linedepicting one or more boundaries between particular nucleus structuresor other regions of the brain, such as the STN, IC, or ZI illustratedabove in FIG. 1B.

In any case, by viewing a representation emphasizing one or more brainregions displayed together with the VOA, the user can then determinewhether a particular anatomic region falls within or outside of themodeled VOA. The user may want a particular anatomic region to beaffected by the DBS, in which case that region should fall within themodeled VOA. Alternatively, the user may want a particular region to beunaffected by the DBS, such as to avoid certain unwanted DBS stimulationside effects, as discussed above. This evaluation of whether the VOA isproperly located can alternatively be performed by, or assisted by, acomputer algorithm.

For example, the computer algorithm can evaluate various VOAs againsteither or both of the following input criteria: (a) one or more regionsin which activation is desired; or (b) one or more regions in whichactivation should be avoided. In one example, at 314, the computeralgorithm creates a score of how such candidate VOAs map against desiredand undesired regions. In one example, the score is computed by countinghow many VOA voxels map to the one or more regions in which activationis desired, then counting how many VOA voxels map to the one or moreregions in which activation is undesired, and subtracting the secondquantity from the first to yield the score. In another example, thesetwo quantities may be weighted differently such as, for example, whenavoiding activation of certain regions is more important than obtainingactivation of other regions (or vice-versa). In yet another example,these two quantities may be used as separate scores.

At 316, the score can be displayed to the user to help the user select aparticular VOA (represented by a particular electrode location andparameter settings). Alternatively, the algorithm can also automaticallyselect the target electrode location and parameter settings that providethe best score for the given input criteria.

In one example, the VOA is displayed on a computer display monitor of animage-guided surgical (IGS) workstation, such as the StealthStation®from the Surgical Navigation Technologies, Inc. (SNT) subsidiary ofMedtronic, Inc., for example. The VOA can be displayed on the IGSworkstation monitor with at least one of the imaging data representingthe anatomic volume, the target electrode location, a burr hole or otheranatomic entry point, a trajectory between the anatomic entry point andthe target electrode location, or an actual electrode location.

In one IGS workstation example, the displayed VOA corresponds to atarget electrode location. Another IGS workstation example provides anintraoperatively displayed VOA corresponds to an actual electrodelocation of an electrode being introduced along the trajectory. The VOAis recomputed and redisplayed as the electrode is being introduced alongthe trajectory, such as by using position information tracking theposition of the electrode being introduced. In one example, various VOAsalong the trajectory are pre-computed, and the particular VOA isselected for display using the tracked position of the electrode as itis being introduced.

After the electrode is positioned at the target location, it istypically secured in place, such as by using a lead immobilizer locatedat the burr hole or other anatomic entry point. There remains thechallenging task of adjusting the DBS stimulation parameters (e.g., theparticular electrode contact(s) of a plurality of electrode contactsdisposed on the same DBS leadwire, pulse amplitude, pulsewidth,electrode “polarity” (i.e., monopolar or bipolar electrode return path),electrode pulse polarity (i.e., positive or negative), frequency, etc.).In one example, the IGS workstation or a DBS pulse generator programmerincludes the above-described VOA methods to assist the user in selectingan appropriate combination of DBS stimulation parameters, such as byusing the scoring techniques discussed above.

FIG. 4 is a block diagram illustrating generally one conceptualizationof a system 400 for performing at least some of the methods discussedabove for DBS or other stimulation of a patient 401. In this example,the system 400 includes an IGS workstation or other computer 402. Thecomputer 402 includes imaging data storage 404 receiving imaging datafrom a medical imaging device 406. In this example, the computer 402also includes DTI or other atlas data storage 408, and a neuron or axonmodel 410, as discussed above. A processor 412 uses a finite elementmodel (FEM) 414 to compute a second difference 416 on an electricpotential distribution. A threshold determination module 418 is used todevelop a threshold value of the second difference 416 of the electricpotential distribution to compute a volume of activation (VOA) 420, asdiscussed above. The processor 412 also includes a scoring module tocompare the VOA against one or more desired or undesired anatomicregions, as discussed above, to determine whether the VOA will performas desired. In one example, the VOA is displayed on a display device424, such as together with other data that is typically displayed on anIGS workstation display, as discussed above. A user input device 426permits a user to input data, for example, particular information aboutthe configuration or morphology of the DBS or other stimulationelectrode 428 being used in the procedure. In one example, a positiontracking device 430 tracks the location of the DBS electrode so that thelocation can be displayed on the display device 424, such as with theVOA or scoring information discussed above. In a further example, thecomputer 402 includes a telemetry circuit 432 for programming orotherwise communicating with an implantable DBS controller circuit 434,such as to adjust electrical stimulation parameters using the VOA orscoring information discussed above. Although FIG. 4 illustrates an IGSworkstation example, it is understood that portions of the system 400could alternatively be implemented outside the context of an IGSworkstation such as, for example, in an external programmer device foran implantable DBS controller circuit 434. Such an alternate exampleneed not include any intraoperative imaging or position tracking.

FIG. 5 is a flow chart illustrating generally one example of a method ofusing a model to calculate a volume of activation, as discussed above,and using the VOA to select a particular electrode morphology. Portionsof the method may be embodied in any machine-accessible medium carryinginstructions for executing acts included in the method. Such a methodapplies to selecting an electrode morphology for deep brain stimulation(DBS) or for any other electrical tissue stimulation. At 500, a set of Ncandidate electrode morphologies are defined, where N is an integergreater than 1. Defining the candidate morphologies typically includesproviding information about the size, shape, or arrangement of electrodecontacts on a leadwire. Such information is typically in a form in whichit can be used as input to a finite element model (FEM). At 502, a FEMis created for each candidate electrode morphology. The FEMs typicallyuse non-uniform conductivity model of a desired region of interest, asdiscussed above. At 504, each FEM is solved for a second difference inthe electric potential distribution, as discussed above. At 506, avolume of activation (VOA) is computed for each candidate electrodemorphology from its corresponding second difference in the electricpotential distribution. The boundary of the VOA is typically obtainedfrom a threshold value that is based on a neuron or axon model, asdiscussed above. At 508, the VOAs are scored, as discussed above, orotherwise evaluated to select one or more electrode morphologies thatexhibit a desired VOA, or a VOA that is deemed more desirable than theVOA of one or more other electrode morphologies. At 510, at least oneelectrode is manufactured using the selected at least one electrodemorphology.

3. Application in a Patient-Specific Neural Stimulation Modeling System

A. Overview

One application of the above-described neural response modelingtechniques is in a patient-specific neural stimulation modeling system(PSNSMS), which can be implemented as a software package that, in oneexample, can be integrated into an IGS workstation or any other desiredcomputer implementation. The PSNSMS allows interactive manipulation ofpatient-specific electrical models of the brain for analysis of brainstimulation methods. This provides a virtual laboratory for surgeons,technicians, or engineers to optimize or otherwise adjust neuralstimulation treatment, such as by varying electrode position,stimulation protocol, or electrode design. In one example, the PSNSMSintegrates data processing, numerical solution and visualization intoone cohesive platform. In one example, the PSNSMS uses a modularframework that incorporates anatomical or functional magnetic resonanceimages, 3D geometric models of individual brain nuclei, volume conductormodels of the electric field generated by the stimulation, biophysicalmodels of the neural response to the stimulation, numerical solutions ofthe coupled electric field and neuron models, and 3D visualization ofthe model results and imaging data. Among other things, the PSNSMSoutputs a volume of influence (neural activation or neural inhibition)generated by the stimulating electrode for a given position in the brainand given stimulation parameters.

Benefits of the PSNSMS may include, among other things: (1)pre-operative targeting of an optimal or desirable neural stimulationelectrode position or trajectory in the brain tissue medium; (2)intra-operative monitoring or visualization of electrode position ortrajectory and stimulation effects as a function of the stimulationparameters; (3) post-operative adjustment or optimization of one or morestimulation parameters for therapeutic benefit given knowledge of theactual electrode position in the brain; or (4) a design tool forevaluating or testing different electrode designs, such as for a givenanatomical target.

Existing techniques for pre-operatively targeting specific nuclei forneurostimulation using magnetic resonance imaging data only account forcertain anatomical considerations. They typically ignore the electricfield generated by the stimulation and the subsequent neural response tothe stimulation. Existing techniques for intra-operatively monitoringthe electrode position in the brain, based on the spontaneous electricalactivity of neurons surrounding the electrode, require highly skilledneurophysiologists to interpret the data. Moreover, such techniques arenot linked with 3D visualization of the surrounding neuroanatomy.Furthermore, they do not enable prediction of the effects of stimulationas a function of the stimulation parameters. Existing techniques fordefining effective stimulation parameter values typically rely on trialand error. They typically do not explicitly take into account theanatomical position of the electrode or the neural response tostimulation as it depends on changes in the stimulation parameters.Moreover, they typically do not use any optimization strategies todefine the stimulator parameter settings.

The PSNSMS addresses these and other limitations. In one example, thePSNSMS uses a finite element model (FEM) of the electric field generatedby the stimulation. In one example, the tissue electrical properties ofthe FEM are based on diffusion tensor magnetic resonance imaginganalysis, also referred to as diffusion tensor imaging (DTI). DTIpermits explicit characterization of the inhomogeneous and anisotropictissue properties near a given electrode position. The inhomogeneous andanisotropic tissue properties distort the electric field. Therefore,they are important to consider when addressing the neural response tothe stimulation.

In one example, the electric field model is then coupled to electricalmodels of individual neurons to predict their response to the appliedstimulation and determine a volume of tissue that is directly influencedby the stimulation. In another example, simplifying assumptions allowthe volume of activation (VOA) to be obtained directly from the electricfield model using the second difference of the potential distribution inthe tissue medium, as discussed above.

The PSNSMS also allows integration of MR imaging data, 3D anatomicalvolumes, neural stimulation electrode trajectory, and 3D neuralstimulation response volume in a single platform or package. Thisplatform or package can be used for, among other things, pre-operativetargeting, intra-operative monitoring, post-operative stimulationparameter adjustment or optimization, or electrode design. One exampleof such a package is a image-guided surgical (IGS) workstation, whichtypically displays voxel data obtained from MR or CT images, and towhich a display of a modeled 3D neural stimulation response volume ofinfluence (or other information obtained from a modeled 3D neuralstimulation response volume of influence) has been added.

B. Exemplary Methods

In one example, the PSNSMS allows, among other things, capture of thedetailed interaction between the electric field and underlyingnon-uniform tissue medium. This enables more accurate estimation of thespatial extent of neural activation generated by one or more electrodesimplanted in the nervous system. In one embodiment, the PSNSMS includesthe following components: (1) a volume conductor electric field modelsuch as a FEM mesh, which includes a model of the stimulating electrodeand of any inhomogeneous and anisotropic electrical properties of nearbytissue; (2) one or more multi-compartment electrical models ofindividual neurons whose positions can be specified within the electricfield (e.g., using anatomically-derived conductivity data to ascertainthe locations of neural pathways) and their response to stimulation canbe quantified; (3) integration of functional or anatomical imaging datainto a visualization platform that can be combined with the electricfield modeling results; or, (4) techniques to determine a desired oroptimal electrode position or one or more desired or optimal stimulationparameters on a patient-specific basis.

FIG. 6 is a block diagram illustrating generally one example of such acomputer-assisted patient-specific neural stimulation modeling system600. In this example, the system 600 includes an electric field model602. In one example, the electric field model 602 is implemented as acomputer-solvable FEM mesh. It typically includes a stimulatingelectrode model 604. The stimulating electrode model 604 typicallyrepresents the morphology of the particular stimulation electrode. Itmay also include a representation of the conductivity of a thin layer oftissue encapsulating the particular electrode. The electric field model602 also includes non-uniform tissue conductivity data 606. Such datarepresents any inhomogeneous or anisotropic properties of the tissuenear the stimulation electrode, and can be obtained by DTI imaging or byusing other techniques described elsewhere in this document.

In the example of FIG. 6, the system 600 also includes a neuron or axonmodel 608. In one example, a multi-compartment neuron or axon modelpositions the modeled neurons or axons at specifiable positions alongone or more nerve pathways in the FEM mesh. Such nerve pathways can beascertained using the DTI-derived imaging data, or by using anatomicatlas data, or any other technique. The example of FIG. 6 also includesstored volumetric imaging data 610 and volumetric anatomic atlas data612. Using a computer FEM solver to solve the electric field model 602,together with the neuron or axon model 608 (optionally using theintermediate step of solving for a second difference in the electricpotential distribution) a volume of influence 614 is calculated. Thevolume of influence 614 typically represents a volume of activation ofregion, but could also represent a volume of inhibition region. Themodel-computed volume of influence 614 is then compared to a targetvolume of influence 616, which, in one example, is specified by userinput that is referenced to the anatomic atlas 612. In one example, acorrelation between the two is computed at 618. In a further example,several model-computed volumes of influence (e.g., using differentelectrode locations or parameter settings) are computed and correlatedto the target volume of influence, such as to optimize or otherwiseselect a desirable electrode location or stimulation parameter settings.The system 600 includes a user interface with a display, such as todisplay the volume of influence in conjunction with the volumetricimaging data 610, which may be annotated or segmented using anatomicboundaries obtained from the anatomic atlas 612, or otherwise. In oneexample, the display also provides an indication of informationregarding the correlation or the optimization.

Our example demonstration of PSNSMS is based on deep brain stimulation(DBS) of the subthalamic nucleus (STN), but the concepts described inthis document are transferable to any electrode design or to stimulationof any region of the nervous system. In one example, one or moreportions of the PSNSMS is constructed using the shareware package SCIRunwith BioPSE (Scientific Computing and Imaging Institute, University ofUtah), which provides an integrated environment for data manipulation,analysis, and interactive visualization.

C. Volume Conductor Electric Field Model Example

In one example, detailed patient-specific electric field models ofcentral nervous system stimulation were developed using anatomical anddiffusion tensor magnetic resonance data (DTI). FIG. 7 is a computerdisplay screenshot illustrating one example of a multi-resolution,finite-element tetrahedral mesh that was constructed to represent thebrain volume near the electrode shaft in the computer display screenshotof FIG. 8. The example of FIG. 7 illustrates a generic mesh thatincludes a high density mesh around the electrode location and a lowdensity mesh located between the high density mesh and the model'speripheral boundary. The finite element method (FEM) allows complexgeometric structures to be accurately represented where analyticalsolutions are complex or fail to exist. The multi-resolution methodillustrated in FIG. 7 provides a dense enough mesh to accurately computethe FEM solution near the electrode, but reduces the size of the systemof equations enough to allow interactive solution of the FEM, such as toexperiment with different stimulation parameters or electrode locations.The accuracy of the solution for a given mesh density can be estimatedusing the L2 norm, and the mesh can be refined as needed.

In one example, the DTI data was used to estimate the inhomogeneous andanisotropic tissue conductivity properties on a patient-specific basisand this information was integrated into the FEM. As described above,the electrical conductivity tensor (σ) was determined from the diffusiontensor (D) at each voxel of the DTI, such as by using Equation 2.

After a pre-operative electrode target location or post-operativeimplanted electrode location is determined, in one example, thevolumetric conductivity data from the DTI (also referred to as a DTIvoxel map) is co-registered with the FEM illustrated in FIG. 6. Thus,the orientation of the coordinate systems of the DTI voxel map and theFEM need not be the same. In one example, the coordinate system of theFEM illustrated in FIG. 7 is defined with its origin at the electrodecontact and its Z-axis extending along the electrode shaft illustratedin FIG. 8. The coordinate system of the DTI voxel map is determined bythe patient head position in the imaging scanner. However, conductivitytensor data is not rotationally invariant. Therefore, in one example, aDTI-based conductivity tensor is rotated from its original acquisitionreference frame to the electrode reference frame, such as by extractingthe electrode angle with respect to the axial (α) and sagittal (β)planes using post-operative anatomical imaging results. In turn, theconductivity tensor used in the FEM (i.e., σ′) is of the form:σ′=RσR ^(T)  (Eq. 3)where R is the rotation matrix for the image transformation defined by αand β.

After this transformation, each node of the FEM mesh is assigned aconductivity tensor that is mapped to its corresponding location withinthe DTI voxel map. In one example, the FEM mesh illustrated in FIG. 7serves as a template structure that can read in σ for each node of theFEM mesh from a generic DTI voxel map data set. This template allows asingle model geometry and FEM mesh (the most difficult and timeconsuming components of FEM development) to be used for eachpatient-specific model and/or for each candidate electrode location towhich each patient-specific model is applied.

After the FEM is defined with the appropriate tissue conductivity data,appropriate boundary conditions are set, as discussed above. Then,Equation 1 is solved to determine the electric potential distributiongenerated in the tissue medium. Equation 1 is typically solved using oneof two solvers, depending on the characteristics of the stimulationwaveform used, an example of which is illustrated in voltage amplitudevs. time graph of FIG. 9. For steady-state analysis, when thequasi-static approximation is valid, the system is typically solvedusing a conjugate gradient solver and constant voltage stimulation.However, if the quasi-static approximation is not valid and bulkcapacitance is to be taken into account, then the system is typicallysolved using a Fourier FEM solver using a time-dependent stimulationwaveform. The Fourier FEM solver decomposes the stimulus waveform into acollection of sine waves, each with known amplitude and phase. Thesesinusoidal sources are added to the right hand side of Equation 1, andcomplex impedances are added to the stiffness matrix (that is, theconductivity tensor (σ)). The system of equations is then solved foreach component frequency using a complex solver. By virtue of linearsuperposition (i.e., the solutions at different frequencies do notsignificantly interact) and assuming small currents (i.e., there is nosignificant coupling between magnetic and electric fields), the solutionfor an arbitrary waveform can be found by summing the time-domainsolutions at each frequency. Solving Equation 1 yields a record of thepotential at each node in the FEM mesh as a function of time during theapplied stimulation.

D. Example of Quantifying the Neural Response to Stimulation

Knowing the potential distribution in the tissue medium alone is notenough to predict the neural response to stimulation. Therefore, in oneexample, we use one or more multi-compartment cable models of individualneurons to address the neural response to the stimulation. Such neuronmodels represent electrically equivalent circuit representations ofphysiological neural signaling mechanisms. The models typically includean explicit representation of the complex neural geometry and individualion channels that regulate generating of action potentials. The neuronmodel geometries are typically broken up into many (e.g., hundreds) ofcompartments and are co-registered within the FEM mesh. This allowscalculation of the extracellular potentials from the applied electricfield along the complex neural geometry. After the extracellularpotentials are determined for each neural compartment as a function oftime during the applied stimulation, for each neural position relativeto the electrode, the model neuron is used to test whether the appliedstimulus exceeded the neural threshold that triggers an actionpotential. The neural response to extracellular stimulation is dependenton several factors, such as, for example: (1) the electrode geometry;(2) the shape of the electric field (as determined by the inhomogeneousand anisotropic bulk tissue properties); (3) the neuron geometry; (4)the neuron position relative to the stimulating electrode; (5) theneuron membrane dynamics; and, (6) the applied stimulation parameters(e.g., stimulus waveform, stimulation frequency, etc.).

In one illustrative example, we used the 5.7 μm diameter double cablemyelinated axon model described in Cameron C. McIntyre et al., “Modelingthe Excitability of Mammalian Nerve Fibers: Influence of Afterpotentialson the Recovery Cycle,” J. Neurophysiology, Vol. 87, February 2002, pp.995-1006, which is incorporated herein by reference in its entirety.(Alternatively, instead of using an axon model, a more detailed neuronalmodel could be used, such as described in Cameron C. McIntyre et al.,“Cellular Effects of Deep Brain Stimulation: Model-Based Analysis ofActivation and Inhibition,” J. Neurophysiology 91: 1457-1469 (2004),which is incorporated by reference herein in its entirety). Weincorporated this model into our STN DBS FEM to quantify the neuralresponse to stimulation.

By positioning the axon in different locations relative to the electrodeand modulating the stimulation parameters one can determine thethreshold stimulus necessary to activate the neuron. Likewise, for agiven stimulation parameter setting (pulse duration, amplitude,frequency), the threshold characteristics of the model neuron can bedetermined as a function of space around the electrode. This informationdefines of a volume of tissue for which the neural activation thresholdis exceeded for the particular stimulation parameter setting. Thisvolume of tissue is referred to as the volume of activation (VOA). Inone example, a further simplification is made by determining a thresholdvalue of the second difference of the potential distribution, which isrepresentative of neural activation for a given stimulation parametersetting, as discussed above and as illustrated in FIG. 2. The thresholdsecond difference value can then also be used to define the VOAboundaries.

When using PSNSMS to pre-operatively characterize stimulation effects,assumptions are typically made as to the appropriate model parametervalues used to predict the volume of activation. However, duringpost-operative use, the PSNSMS model can be fit to patient-specificexperimental threshold results. The tissue conductivity and electrodelocalization for each patient-specific FEM can be adjusted to match theclinically determined threshold stimulation results for activating majorfiber tracts near the electrode. Detecting fiber tract activation mayinvolve monitoring behavioral responses that are known to arise fromsuch activation of specific fiber tracts. The clinical threshold toelicit these behavioral responses is determined. These fiber tracts canbe explicitly visualized on the DTI voxel map. The location andtrajectory of particular fiber tracts can be directly integrated intoPSNSMS by positioning the axon models along the appropriate anatomicaltrajectory in the FEM. Three general variables can be adjusted to fitthe FEM to the experimental data. First, the conductivity of theencapsulation layer about the electrode can be adjusted (e.g., 0.2S/m<σ_(encap)<0.1 S/m) to fit the FEM to the experimental data.Alterations in this variable modulate the electrode input impedance.Such adjustments can be guided by clinical data from the stimulatorprogramming unit. Second, the ratio of effective extracellularconductivity and the effective extracellular diffusivity(0.6<σ_(e)/d_(e)<0.8 (S-s)/mm²) can be adjusted. Altering this variablescales the absolute value of the conductivity tensor and modulates thestimulus amplitudes needed for axonal activation. A third variable isthe X, Y, Z position of the electrode relative to the tissue medium. Weexpect about 1 mm error in our MR-based electrode localization due tothe metallic distortion artifact generated by the electrode in the MRimage. Therefore, in one example, we allow the electrode to be shiftedby a maximum of 0.5 mm in any direction to allow convergence between themodel-predicted threshold data and the clinical threshold data.

E. Example of Integrating Stimulation Modeling Results and AnatomicImaging Data

Calculating the volume of activation as a function of the electrodelocation and stimulation parameters represents one component of PSNSMS.This provides even greater utility when it is integrated withpatient-specific anatomical data. Anatomical data derived from MRI iscommonly used to target stereotactic neurosurgical procedures. In oneexample, however, the PSNSMS integrates and displays the anatomical datawith volume of activation data, as illustrated by the computer displayscreenshot of FIG. 10. In FIG. 10, the PSNSMS outputs a volume-renderedor other data display indicating one or more of: one or more non-targetregions 1000 of the brain; the target region 1002 of the brain (in thiscase, the STN); or the volume of influence (e.g., volume of activationor volume of inhibition) 1004. In the example of FIG. 10, these also arecoregistered to and displayed with the DBS electrode catheter 1006.

In one example, the PSNSMS includes a patient-specific brain atlas. Suchatlases can be generated from the pre-operative anatomical MR imagesusing techniques originally described by Gary E. Christensen et al.,“Volumetric Transformation of Brain Anatomy,” IEEE Trans. on MedicalImaging, Vol. 16, No. 6, pp. 864-877, December 1997, which isincorporated herein by reference in its entirety. However, any varietyof morphing algorithms could be used. One suitable algorithm includes anonlinear transformation to register one MRI (the patient-specificimage) to a second pre-labeled target MRI that serves as a canonicalatlas for particular regions of the brain. Segmentation of thepatient-specific MRI is achieved by using the inverse of thistransformation to warp the canonical atlas back onto the patient's 3D MRimage. In one example, the registration procedure operates in twostages. In the first stage, a low-dimensional registration isaccomplished by constraining the transformation to be in alow-dimensional basis. In one example, the basis is defined by theGreen's function of the elasticity operator placed at pre-definedlocations in the anatomy and the eigenfunctions of the elasticityoperator. In the second stage, high-dimensional large transformationsare vector fields generated via the mismatch between the template andtarget-image volumes constrained to be the solution of a Navier-Stokesfluid model. The result of these transformations is a 3D brain atlasmatched to the individual patient with specific volumes representingpre-labeled target nuclei. The 3D surface data derived from thepatient-specific brain atlas is then co-registered and, in one example,is displayed with the electrode and volume of activation data, asillustrated in the example of FIG. 10.

F. Example of Model-Based Selection of Patient-Specific Target ElectrodeLocations or Stimulation Parameter Settings

One purpose of the PSNSMS is to determine optimal or desirablepreoperative electrode locations or post-operative optimal or desirablestimulation parameters settings on a patient-specific basis. Thistypically involves determining a target volume of tissue that should beactivated by the stimulation. In the PSNSMS, the geometry of this targetVOA is typically determined based on the patient-specific 3D brainatlas. For example, in the case of STN DBS for Parkinson's disease,current anatomical and physiological knowledge indicate that the targetvolume of tissue is the dorsal half of the STN. Therefore, in thisexample, for each patient-specific 3D brain atlas we determine a targetVOA defined by the dorsal half of the STN. We then determine test VOAsgenerated by a range of electrode positions within the STN and/or arange of stimulation parameter settings for each of those electrodelocations. These test VOAs are then compared to the target VOA. Theelectrode position and/or stimulation parameter setting that generates atest VOA that most closely matches the target VOA is provided as themodel-selected electrode position and/or stimulation parameter setting.

In one variant of this selection process, engineering optimization isused to assist the selection process. Examples of possible constraintson the selection process include one or more of minimizing chargeinjection into the tissue, minimizing spread of the test VOA outside ofthe target VOA, maximizing overlap between the test VOA and target VOA,limiting the stimulus amplitude to being greater than −10 V and lessthen 10V, limiting the stimulus pulse duration to being greater than 0and less than 450 ms, limiting the stimulation frequency to beinggreater than 0 and less than 185 Hz. In one such example, limits on thestimulation parameters are determined by the output of the currentclinical stimulator. Therefore, if new technology provides new outputlimits, our model limits could be refined to reflect these changes. In afurther example, the engineering optimization uses one or more penaltyfunctions, which can be applied for test VOAs that spread intoneighboring anatomical structures that are known to induce side effects.

When using PSNSMS pre-operatively, in one example, both the electrodelocation and stimulation parameters can be varied to determine test VOAsthat match the target VOA. This helps determine a pre-operative targetfor stereotactic neurosurgical implantation of the electrode.

When using PSNSMS post-operatively, in one example, the modeledelectrode position in the tissue medium is established using the actualimplanted electrode location. Then, one or more stimulation parametersare varied to determine test VOAs, which are compared to the target VOAto determine which test VOA (and hence, which parameter setting(s))obtain the closest match to the target VOA. This indicates which chronicstimulation parameter settings maximize or otherwise provide the desiredtherapeutic benefit.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments (and/or aspects thereof) may be used in combination witheach other. Many other embodiments will be apparent to those of skill inthe art upon reviewing the above description, and aspects of describedmethods will be computer-implementable as instructions on amachine-accessible medium. The scope of the invention should, therefore,be determined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled. In the appendedclaims, the terms “including” and “in which” are used as theplain-English equivalents of the respective terms “comprising” and“wherein.” Also, in the following claims, the terms “including” and“comprising” are open-ended, that is, a system, device, article, orprocess that includes elements in addition to those listed after such aterm in a claim are still deemed to fall within the scope of that claim.Moreover, in the following claims, the terms “first,” “second,” and“third,” etc. are used merely as labels, and are not intended to imposenumerical requirements on their objects.

1. A method of displaying a predicted volume of influence by anelectrode inserted inside a patient's neural tissue, the methodcomprising: receiving imaging data representing the anatomy of thepatient's neural tissue; creating a patient-specific atlas of neuraltissue that is specific to the patient by registering the imaging datawith an atlas that is not specific to the patient and that identifiesneural structures in neural tissue; calculating, by a computerprocessor, an electric field in the patient's neural tissue, theelectric field to be generated by the electrode; calculating, by theprocessor and based on the determined electric field, a predicted volumeof influence for the electrode inserted inside the patient's neuraltissue, wherein the predicted volume of influence is a representation ofestimated electrode activated neural tissue; and displaying, on adisplay screen, the predicted volume of influence together with thepatient-specific atlas of neural tissue.
 2. The method of claim 1,wherein the step of registering the imaging data with the atlas that isnot specific to the patient comprises performing a transformation of theatlas that is not specific to the patient.
 3. The method of claim 2,wherein the transformation is performed using pre-defined locations inthe anatomy of the patient's neural tissue as represented by the imagingdata.
 4. The method of claim 3, wherein the transformation is performedusing an elasticity operator placed at the pre-defined locations in theanatomy of the patient's neural tissue as represented by the imagingdata.
 5. The method of claim 2, wherein the transformation is anon-linear transformation.
 6. The method of claim 2, wherein thetransformation is performed by: performing a first transformation havinga first dimensional basis; and performing a second transformation havinga second higher dimensional basis than the first dimensional basis. 7.The method of claim 1, wherein the step of registering the imaging datawith the non-patient-specific atlas comprises performing atransformation of the imaging data.
 8. The method of claim 7, whereinthe transformation is performed using pre-defined locations in theanatomy of the patient's neural tissue as represented by the imagingdata.
 9. The method of claim 8, wherein the transformation is performedusing an elasticity operator placed at the pre-defined locations in theanatomy of the patient's neural tissue as represented by the imagingdata.
 10. The method of claim 7, wherein the transformation is anon-linear transformation.
 11. The method of claim 7, wherein thetransformation is performed by: performing a first transformation havinga first dimensional basis; and performing a second transformation havinga second higher dimensional basis than the first dimensional basis. 12.The method of claim 1, wherein the patient-specific atlas includes theidentification of a specific volume representing a neural structure inthe patient's neural tissue.
 13. The method of claim 12, wherein thepatient's neural tissue and the neural tissue whose neural structuresare identified by the atlas that is not specific to the patient is thebrain, and wherein the neural structure is a brain nuclei.
 14. Themethod of claim 1, wherein the patient's neural tissue and the neuraltissue whose neural structures are identified by the atlas that is notspecific to the patient is the brain.
 15. The method of claim 1, whereinthe non-patient-specific atlas is provided as an MR image.
 16. Themethod of claim 1, wherein the atlas that is not specific to the patientand the imaging data each comprise a plurality of two-dimensionalimages.
 17. The method of claim 16, wherein each of the plurality oftwo-dimensional images comprises magnetic resonance images of thepatient's neural tissue.
 18. The method of claim 1, wherein the imagingdata comprises a plurality of pre-operative magnetic resonance images ofthe patient's neural tissue.
 19. The method of claim 1, wherein thepredicted volume of influence is a function of the location of theelectrode relative to the patient's neural tissue.
 20. The method ofclaim 1, wherein the calculating includes using patient-specificinformation obtained from the patient to calculate the predicted volumeof influence.
 21. The method of claim 1, wherein the calculatingincludes using the imaging data.
 22. The method of claim 1, wherein thecalculating includes using a computational model of neurons.
 23. Amethod of displaying a predicted volume of influence by an electrodeinserted inside a patient's neural tissue, the method comprising:receiving imaging data representing the anatomy of the patient's neuraltissue; creating a patient-specific atlas of neural tissue byregistering the imaging data with a non-patient-specific atlas thatidentifies neural structures in neural tissue; calculating, by acomputer processor, a predicted volume of influence for the electrodeinserted inside the patient's neural tissue; and displaying, on adisplay screen, the predicted volume of influence together with thepatient-specific atlas of neural tissue; wherein: the step ofregistering the imaging data with the non-patient-specific atlascomprises performing a transformation of the non-patient-specific atlas;and the transformation is performed by: performing a firsttransformation having a first dimensional basis; and performing a secondtransformation having a second higher dimensional basis than the firstdimensional basis, the second transformation including the use of vectorfields generated using a mismatch between the non-patient-specific atlasand the anatomy of the patient's neural tissue as represented by theimaging data.
 24. The method of claim 23, wherein the mismatch isconstrained to be a solution to a Navier-Stokes fluid model.
 25. Amethod of displaying a predicted volume of influence by an electrodeinserted inside a patient's neural tissue, the method comprising:receiving imaging data representing the anatomy of the patient's neuraltissue; creating a patient-specific atlas of neural tissue byregistering the imaging data with a non-patient-specific atlas thatidentifies neural structures in neural tissue; calculating, by acomputer processor, a predicted volume of influence for the electrodeinserted inside the patient's neural tissue; and displaying, on adisplay screen, the predicted volume of influence together with thepatient-specific atlas of neural tissue; wherein: the step ofregistering the imaging data with the non-patient-specific atlascomprises performing a transformation of the imaging data; and thetransformation is performed by: performing a first transformation havinga first dimensional basis; and performing a second transformation havinga second higher dimensional basis than the first dimensional basis, thesecond transformation including the use of vector fields generated usinga mismatch between the non-patient-specific atlas and the anatomy of thepatient's neural tissue as represented by the imaging data.
 26. Themethod of claim 25, wherein the mismatch is constrained to be a solutionto a Navier-Stokes fluid model.
 27. A computer system comprising: (a) aprocessor configured to receive imaging data representing the anatomy ofa patient's neural tissue; (b) a memory device in which an atlas that isnot specific to the patient and that identifies neural structures inneural tissue is stored; and (c) a display screen; wherein the processoris configured to perform steps comprising: creating a patient-specificatlas of neural tissue that is specific to the patient by registeringthe imaging data with the atlas that is not specific to the patient;calculating an electric field in the patient's neural tissue, theelectric field to be generated by an electrode inserted inside thepatient's neural tissue; calculating based on the determined electricfield, a predicted volume of influence for the electrode inserted insidethe patient's neural tissue, wherein the predicted volume of influenceis a representation of estimated electrode activated neural tissue; anddisplaying, on the display screen, the predicted volume of influencetogether with the patient-specific atlas of neural tissue.
 28. Anon-transitory computer-readable storage medium comprising instructionsfor: receiving imaging data representing the anatomy of a patient'sneural tissue; accessing an atlas that is not specific to the patientand that identifies neural structures in neural tissue; creating apatient-specific atlas of neural tissue that is specific to the patientby registering the imaging data with the atlas that is not specific tothe patient; calculating an electric field in the patient's neuraltissue, the electric field to be generated by an electrode insertedinside the patient's neural tissue; calculating based on the determinedelectric field, a predicted volume of influence for the electrodeinserted inside the patient's neural tissue, wherein the predictedvolume of influence is a representation of estimated electrode activatedneural tissue; and displaying the predicted volume of influence togetherwith the patient-specific atlas of neural tissue.
 29. A method ofdisplaying a predicted volume of influence by an electrode insertedinside a patient's neural tissue, the method comprising: receivingimaging data representing the anatomy of the patient's neural tissue;creating a patient-specific atlas of neural tissue that is specific tothe patient by registering the imaging data with an atlas that is notspecific to the patient and that identifies neural structures in neuraltissue, the registering including performing a transformation of theatlas that is not specific to the patient; calculating, by a computerprocessor, a predicted volume of influence for the electrode insertedinside the patient's neural tissue, wherein the predicted volume ofinfluence is a representation of estimated electrode activated neuraltissue; and displaying, on a display screen, the predicted volume ofinfluence together with the patient-specific atlas of neural tissue;wherein: the transformation is performed by: performing a firsttransformation having a first dimensional basis; and performing a secondtransformation having a second higher dimensional basis than the firstdimensional basis; and performing the second transformation includes theuse of vector fields generated using a mismatch between the atlas thatis not specific to the patient and the anatomy of the patient's neuraltissue as represented by the imaging data.
 30. The method of claim 29,wherein the mismatch is constrained to be a solution to a Navier-Stokesfluid model.
 31. A method of displaying a predicted volume of influenceby an electrode inserted inside a patient's neural tissue, the methodcomprising: receiving imaging data representing the anatomy of thepatient's neural tissue; creating a patient-specific atlas of neuraltissue that is specific to the patient by registering the imaging datawith an atlas that is not specific to the patient and that identifiesneural structures in neural tissue, the registering including performinga transformation of the imaging data; calculating, by a computerprocessor, a predicted volume of influence for the electrode insertedinside the patient's neural tissue, wherein the predicted volume ofinfluence is a representation of estimated electrode activated neuraltissue; and displaying, on a display screen, the predicted volume ofinfluence together with the patient-specific atlas of neural tissue;wherein: the transformation is performed by: performing a firsttransformation having a first dimensional basis; and performing a secondtransformation having a second higher dimensional basis than the firstdimensional basis; performing the second transformation includes the useof vector fields generated using a mismatch between the atlas that isnot specific to the patient and the anatomy of the patient's neuraltissue as represented by the imaging data.
 32. The method of claim 31,wherein the mismatch is constrained to be a solution to a Navier-Stokesfluid model.
 33. A method of displaying a predicted volume of influenceby an electrode inserted inside a patient's neural tissue, the methodcomprising: receiving imaging data representing the anatomy of thepatient's neural tissue; creating a patient-specific atlas of neuraltissue that is specific to the patient by registering the imaging datawith an atlas that is not specific to the patient and that identifiesneural structures in neural tissue; calculating, by a computerprocessor, a predicted volume of influence for the electrode insertedinside the patient's neural tissue, wherein the predicted volume ofinfluence is a representation of estimated electrode activated neuraltissue; and displaying, on a display screen, the predicted volume ofinfluence together with the patient-specific atlas of neural tissue;wherein the calculating includes using electric potential distribution.34. A method of displaying a predicted volume of influence by anelectrode inserted inside a patient's neural tissue, the methodcomprising: receiving imaging data representing the anatomy of thepatient's neural tissue; creating a patient-specific atlas of neuraltissue that is specific to the patient by registering the imaging datawith an atlas that is not specific to the patient and that identifiesneural structures in neural tissue; calculating, by a computerprocessor, a predicted volume of influence for the electrode insertedinside the patient's neural tissue, wherein the predicted volume ofinfluence is a representation of estimated electrode activated neuraltissue; and displaying, on a display screen, the predicted volume ofinfluence together with the patient-specific atlas of neural tissue;wherein the calculating includes using tissue conductivity data.